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ICMCS
2009
IEEE

Some new directions in graph-based semi-supervised learning

8 years 1 months ago
Some new directions in graph-based semi-supervised learning
In this position paper, we first review the state-of-the-art in graph-based semi-supervised learning, and point out three limitations that are particularly relevant to multimedia analysis: (1) rich data is restricted to live on a single manifold; (2) learning must happen in batch mode; and (3) the target label is assumed smooth on the manifold. We then discuss new directions in semi-supervised learning research that can potentially overcome these limitations: (i) modeling data as a mixture of multiple manifolds that may intersect or overlap; (ii) online semi-supervised learning that learns incrementally with low computation and memory needs; and (iii) learning spectrally sparse but non-smooth labels with compressive sensing. We give concrete examples in each new direction. We hope this article will inspire new research that makes semi-supervised learning an even more valuable tool for multimedia analysis.
Xiaojin Zhu, Andrew B. Goldberg, Tushar Khot
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICMCS
Authors Xiaojin Zhu, Andrew B. Goldberg, Tushar Khot
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